OpenRaiser/Intern-Atlas huggingface dataset:
Particle-based pedestrian path prediction using LSTM-MDL models
Venue: arXiv cs.CV 2018
Year: 2018

Recurrent neural networks are able to learn complex long-term relationships from sequential data and output a pdf over the state space. Therefore, recurrent models are a natural choice to address path prediction tasks, where a trained model is used to generate future expectations from past observations. When applied to security applications, like predicting the path of pedestrians for risk assessment, a point-wise greedy (ML) evaluation of the output pdf is not feasible, since the environment often allows multiple choices. Therefore, a robust risk assessment has to take all options into account, even if they are overall not very likely. Towards this end, a combination of particle filter sampling strategies and a LSTM-MDL model is proposed to address a multi-modal path prediction task. The capabilities and viability of the proposed approach are evaluated on several synthetic test conditions, yielding the counter-intuitive result that the simplest approach performs best. Further, the feasibility of the proposed approach is illustrated on several real world scenes.
Biological Optical-to-Chemical Signal Conversion Interface: A Small-scale Modulator for Molecular Communications
Venue: arXiv cs.ET 2018
Year: 2018

Although many exciting applications of molecular communication (MC) systems are envisioned to be at microscale, the available MC testbeds reported in the literature so far are mostly at macroscale. This may partially be due to the fact that controlling an MC system at microscale is quite challenging. To link the macroworld to the microworld, we propose a biological signal conversion interface that can also be seen as a microscale modulator. This interface translates an optical signal, which can be easily controlled using a light-emitting diode (LED), into a chemical signal by changing the pH of the environment. The modulator is realized using \textit{Escherichia coli} bacteria that express the light-driven proton pump gloeorhodopsin from \textit{Gloeobacter violaceus}. Upon inducing external light stimuli, these bacteria can locally change their surrounding pH level by exporting protons into the environment. Based on measurement data from a testbed, we develop an analytical model for the induced chemical signal as a function of the applied optical signal. Finally, using a pH sensor as detector, we show for an example scenario that the proposed setup is able to successfully convert an optical signal representing a sequence of binary symbols into a chemical signal with a bit rate of $1$~bit/min.
Adaptive MapReduce Similarity Joins
Venue: arXiv cs.DS 2018
Year: 2018

Similarity joins are a fundamental database operation. Given data sets S and R, the goal of a similarity join is to find all points x in S and y in R with distance at most r. Recent research has investigated how locality-sensitive hashing (LSH) can be used for similarity join, and in particular two recent lines of work have made exciting progress on LSH-based join performance. Hu, Tao, and Yi (PODS 17) investigated joins in a massively parallel setting, showing strong results that adapt to the size of the output. Meanwhile, Ahle, Aumüller, and Pagh (SODA 17) showed a sequential algorithm that adapts to the structure of the data, matching classic bounds in the worst case but improving them significantly on more structured data. We show that this adaptive strategy can be adapted to the parallel setting, combining the advantages of these approaches. In particular, we show that a simple modification to Hu et al.'s algorithm achieves bounds that depend on the density of points in the dataset as well as the total outsize of the output. Our algorithm uses no extra parameters over other LSH approaches (in particular, its execution does not depend on the structure of the dataset), and is likely to be efficient in practice.
Design and implementation of a wireless instrument adapter
Venue: arXiv cs.RO 2018
Year: 2018

The evaluation of new methods for control and manipulation in minimally invasive robotic surgery requires a realistic setup. To decouple the evaluation of methods from overall clinical systems, we propose an instrument adapter for the S line EndoWrist\c{opyright} instruments of the da Vinci surgical system. The adapter is small and lightweight and can be mounted to any robot to mimic motion. We describe its design and implementation, as well as a setup to calibrate instruments to study precise motion control. Our results indicate that each instrument requires individual calibration. The calibration shows that the system is not fully linear. The repeatability of poses in the same sense of rotation has an RMSE of 0.27°/ and a standard deviation below 0.3° for pitching and 4.7° for yawing averaged over three measurements. When comparing the same poses in clockwise and counter-clockwise direction the RMSE is 12.8° and 5.7° for pitching and yawing, respectively. This is likely due to motor hysteresis.
Trees from Functions as Processes
Venue: arXiv cs.LO 2018
Year: 2018

Levy-Longo Trees and Bohm Trees are the best known tree structures on the λ-calculus. We give general conditions under which an encoding of the λ-calculus into the π-calculus is sound and complete with respect to such trees. We apply these conditions to various encodings of the call-by-name λ-calculus, showing how the two kinds of tree can be obtained by varying the behavioural equivalence adopted in the π-calculus and/or the encoding.
Bend-minimum Orthogonal Drawings in Quadratic Time
Venue: arXiv cs.DS 2018
Year: 2018

Let $G$ be a planar $3$-graph (i.e., a planar graph with vertex degree at most three) with $n$ vertices. We present the first $O(n^2)$-time algorithm that computes a planar orthogonal drawing of $G$ with the minimum number of bends in the variable embedding setting. If either a distinguished edge or a distinguished vertex of $G$ is constrained to be on the external face, a bend-minimum orthogonal drawing of $G$ that respects this constraint can be computed in $O(n)$ time. Different from previous approaches, our algorithm does not use minimum cost flow models and computes drawings where every edge has at most two bends.
ClaiRE at SemEval-2018 Task 7 - Extended Version
Venue: arXiv cs.CL 2018
Year: 2018

In this paper we describe our post-evaluation results for SemEval-2018 Task 7 on clas- sification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). This is an extended ver- sion of our workshop paper (Hettinger et al., 2018) including further technical details (Sec- tions 3.2 and 4.3) and changes made to the preprocessing step in the post-evaluation phase (Section 2.1). Due to these changes Classification of Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75.11% for the first subtask and 81.44% for the second.
Towards High Performance Video Object Detection for Mobiles
Venue: arXiv cs.CV 2018
Year: 2018

Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. It is also unclear whether the key principles of sparse feature propagation and multi-frame feature aggregation apply at very limited computational resources. In this paper, we present a light weight network architecture for video object detection on mobiles. Light weight image object detector is applied on sparse key frames. A very small network, Light Flow, is designed for establishing correspondence across frames. A flow-guided GRU module is designed to effectively aggregate features on key frames. For non-key frames, sparse feature propagation is performed. The whole network can be trained end-to-end. The proposed system achieves 60.2% mAP score at speed of 25.6 fps on mobiles (e.g., HuaWei Mate 8).
Investigating Cybersecurity Issues In Active Traffic Management Systems
Venue: arXiv cs.CR 2018
Year: 2018

Active Traffic Management (ATM) systems have been introduced by transportation agencies to manage recurrent and non-recurrent congestion. ATM systems rely on the interconnectivity of components made possible by wired and/or wireless networks. Unfortunately, this connectivity that supports ATM systems also provides potential system access points that results in vulnerability to cyberattacks. This is becoming more pronounced as ATM systems begin to integrate internet of things (IoT) devices. Hence, there is a need to rigorously evaluate ATM systems for cyberattack vulnerabilities, and explore design concepts that provide stability and graceful degradation in the face of cyberattacks. In this research, a prototype ATM system along with a real-time cyberattack monitoring system were developed for a 1.5-mile section of I-66 in Northern Virginia. The monitoring system detects deviation from expected operation of an ATM system by comparing lane control states generated by the ATM system with lane control states deemed most likely by the monitoring system. In case of any deviation between two sets of states, the monitoring system displays the lane control states generated by the back-up data source. In a simulation experiment, the prototype ATM system and cyberattack monitoring system were subject to emulated cyberattacks. The evaluation results showed that when subject to cyberattack, the mean speed reduced by 15% compared to the case with the ATM system and was similar to the baseline case. This illustrates that the effectiveness of the ATM system was negated by cyberattacks. The monitoring system however, allowed the ATM system to revert to an expected safe state and reduced the negative impact of cyberattacks. These results illustrate the need to revisit ATM system design concepts as a means to protect against cyberattacks in addition to traditional system intrusion prevention approaches.
Towards Robust Monitoring of Stealthy Diffusion
Venue: arXiv cs.GT 2018
Year: 2018

In this work, we introduce and study the \emph{$(α, β)$-Monitoring} game on networks. Our game is composed of two parties an attacker and a defender. The attacker can launch an attack by distributing a limited number of seeds (i.e., virus) to the network, and/or manipulate the propagation probabilities on a limited number of edges. Under our $(α, β)$-Monitoring game, we say an attack is successful if and only if the following two conditions are satisfied: (1) the outbreak/propagation reaches $α$ individuals, and (2) it has not been detected before reaching $β$ individuals. On the other end, the defender's ultimate goal is to deploy a set of monitors in the network that can minimize attacker's success ratio in the worst-case. Our work is built upon recent work in security games, compared with stochastic guarantees, our adversarial setting leads to more robust solutions in practice.
Source-channel separation for two-way interactive communication with fidelity criteria
Venue: arXiv cs.IT 2018
Year: 2018

Consider the channel coding problem where two users are interacting in order to communicate an i.i.d. source X1 from User 1 to User 2 with distortion D1 and an i.i.d. source X2 from User 2 to User 1 with distortion D2. X1 and X2 may be dependent. Communication occurs from User 1 to User 2 via a DMC C1 and from User 2 to User 1 over a DMC C2, where C1 and C2 act independently of each other. Communication occurs during each time slot between both users and each user can make a coding and decoding based on all past available knowledge. This interactive communication problem is formulated and it is proved that source-channel separation based architectures are optimal.
A New Decidable Class of Tuple Generating Dependencies: The Triangularly-Guarded Class
Venue: arXiv cs.AI 2018
Year: 2018

In this paper we introduce a new class of tuple-generating dependencies (TGDs) called triangularly-guarded TGDs, which are TGDs with certain restrictions on the atomic derivation track embedded in the underlying rule set. We show that conjunctive query answering under this new class of TGDs is decidable. We further show that this new class strictly contains some other decidable classes such as weak-acyclic, guarded, sticky and shy, which, to the best of our knowledge, provides a unified representation of all these aforementioned classes.
Joint Quantizer Optimization based on Neural Quantizer for Sum-Product Decoder
Venue: arXiv cs.IT 2018
Year: 2018

A low-precision analog-to-digital converter (ADC) is required to implement a frontend device of wideband digital communication systems in order to reduce its power consumption. The goal of this paper is to present a novel joint quantizer optimization method for minimizing lower-precision quantizers matched to the sum-product algorithms. The principal idea is to introduce a quantizer that includes a feed-forward neural network and the soft staircase function. Since the soft staircase function is differentiable and has non-zero gradient values everywhere, we can exploit backpropagation and a stochastic gradient descent method to train the feed-forward neural network in the quantizer. The expected loss regarding the channel input and the decoder output is minimized in a supervised training phase. The experimental results indicate that the joint quantizer optimization method successfully provides an 8-level quantizer for a low-density parity-check (LDPC) code that achieves only a 0.1-dB performance loss compared to the unquantized system.
God Save the Queen
Venue: arXiv cs.MA 2018
Year: 2018

Queen Daniela of Sardinia is asleep at the center of a round room at the top of the tower in her castle. She is accompanied by her faithful servant, Eva. Suddenly, they are awakened by cries of "Fire". The room is pitch black and they are disoriented. There is exactly one exit from the room somewhere along its boundary. They must find it as quickly as possible in order to save the life of the queen. It is known that with two people searching while moving at maximum speed 1 anywhere in the room, the room can be evacuated (i.e., with both people exiting) in $1 + \frac{2π}{3} + \sqrt{3} \approx 4.8264$ time units and this is optimal~[Czyzowicz et al., DISC'14], assuming that the first person to find the exit can directly guide the other person to the exit using her voice. Somewhat surprisingly, in this paper we show that if the goal is to save the queen (possibly leaving Eva behind to die in the fire) there is a slightly better strategy. We prove that this "priority" version of evacuation can be solved in time at most $4.81854$. Furthermore, we show that any strategy for saving the queen requires time at least $3 + π/6 + \sqrt{3}/2 \approx 4.3896$ in the worst case. If one or both of the queen's other servants (Biddy and/or Lili) are with her, we show that the time bounds can be improved to $3.8327$ for two servants, and $3.3738$ for three servants. Finally we show lower bounds for these cases of $3.6307$ (two servants) and $3.2017$ (three servants). The case of $n\geq 4$ is the subject of an independent study by Queen Daniela's Royal Scientific Team.
Ontologies for Representing Relations among Political Agents
Venue: arXiv cs.CY 2018
Year: 2018

The Internet and the Web are now an integral part of the way most modern societies, and corresponding political systems, work. We regard Political systems as the formal and informal political processes by which decisions are made concerning the use, production and distribution of resources in any given society. Our focus in on the sets of agents - Persons and Organizations - that govern a society, and their relations. We present a set of ontologies aimed at characterizing different kinds of direct and indirect relations that occur within a Political System. The goal is to provide a more semantically precise basis for determining more abstract notions such as "influence". These ontologies are being used for the "Se Liga na Politica" project, whose goal is to provide an open linked data database of Political Agents in Brazil. Whereas they are being used in a particular political system, these ontologies can be applied to different political systems.
Model-Free Linear Quadratic Control via Reduction to Expert Prediction
Venue: arXiv cs.LG 2018
Year: 2018

Model-free approaches for reinforcement learning (RL) and continuous control find policies based only on past states and rewards, without fitting a model of the system dynamics. They are appealing as they are general purpose and easy to implement; however, they also come with fewer theoretical guarantees than model-based RL. In this work, we present a new model-free algorithm for controlling linear quadratic (LQ) systems, and show that its regret scales as $O(T^{ξ+2/3})$ for any small $ξ>0$ if time horizon satisfies $T>C^{1/ξ}$ for a constant $C$. The algorithm is based on a reduction of control of Markov decision processes to an expert prediction problem. In practice, it corresponds to a variant of policy iteration with forced exploration, where the policy in each phase is greedy with respect to the average of all previous value functions. This is the first model-free algorithm for adaptive control of LQ systems that provably achieves sublinear regret and has a polynomial computation cost. Empirically, our algorithm dramatically outperforms standard policy iteration, but performs worse than a model-based approach.
Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows
Venue: arXiv cs.DC 2018
Year: 2018

Distributed computing platforms provide a robust mechanism to perform large-scale computations by splitting the task and data among multiple locations, possibly located thousands of miles apart geographically. Although such distribution of resources can lead to benefits, it also comes with its associated problems such as rampant duplication of file transfers increasing congestion, long job completion times, unexpected site crashing, suboptimal data transfer rates, unpredictable reliability in a time range, and suboptimal usage of storage elements. In addition, each sub-system becomes a potential failure node that can trigger system wide disruptions. In this vision paper, we outline our approach to leveraging Deep Learning algorithms to discover solutions to unique problems that arise in a system with computational infrastructure that is spread over a wide area. The presented vision, motivated by a real scientific use case from Belle II experiments, is to develop multilayer neural networks to tackle forecasting, anomaly detection and optimization challenges in a complex and distributed data movement environment. Through this vision based on Deep Learning principles, we aim to achieve reduced congestion events, faster file transfer rates, and enhanced site reliability.
Rafiki: Machine Learning as an Analytics Service System
Venue: arXiv cs.DB 2018
Year: 2018

Big data analytics is gaining massive momentum in the last few years. Applying machine learning models to big data has become an implicit requirement or an expectation for most analysis tasks, especially on high-stakes applications.Typical applications include sentiment analysis against reviews for analyzing on-line products, image classification in food logging applications for monitoring user's daily intake and stock movement prediction. Extending traditional database systems to support the above analysis is intriguing but challenging. First, it is almost impossible to implement all machine learning models in the database engines. Second, expertise knowledge is required to optimize the training and inference procedures in terms of efficiency and effectiveness, which imposes heavy burden on the system users. In this paper, we develop and present a system, called Rafiki, to provide the training and inference service of machine learning models, and facilitate complex analytics on top of cloud platforms. Rafiki provides distributed hyper-parameter tuning for the training service, and online ensemble modeling for the inference service which trades off between latency and accuracy. Experimental results confirm the efficiency, effectiveness, scalability and usability of Rafiki.
Temporal Coherent and Graph Optimized Manifold Ranking for Visual Tracking
Venue: arXiv cs.CV 2018
Year: 2018

Recently, weighted patch representation has been widely studied for alleviating the impact of background information included in bounding box to improve visual tracking results. However, existing weighted patch representation models generally exploit spatial structure information among patches in each frame separately which ignore (1) unary featureof each patch and (2) temporal correlation among patches in different frames. To address this problem, we propose a novel unified temporal coherence and graph optimized ranking model for weighted patch representation in visual tracking problem. There are three main contributions of this paper. First, we propose to employ a flexible graph ranking for patch weight computation which exploits both structure information among patches and unary feature of each patch simultaneously. Second, we propose a new more discriminative ranking model by further considering the temporal correlation among patches in different frames. Third, a neighborhood preserved, low-rank graph is learned and incorporated to build a unified optimized ranking model. Experiments on two benchmark datasets show the benefits of our model.
Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment
Venue: arXiv cs.AI 2018
Year: 2018

Automated vehicles are deemed to be the key element for the intelligent transportation system in the future. Many studies have been made to improve the Automated vehicles' ability of environment recognition and vehicle control, while the attention paid to decision making is not enough though the decision algorithms so far are very preliminary. Therefore, a framework of the decision-making training and learning is put forward in this paper. It consists of two parts: the deep reinforcement learning training program and the high-fidelity virtual simulation environment. Then the basic microscopic behavior, car-following, is trained within this framework. In addition, theoretical analysis and experiments were conducted on setting reward function for accelerating training using deep reinforcement learning. The results show that on the premise of driving comfort, the efficiency of the trained Automated vehicle increases 7.9% compared to the classical traffic model, intelligent driver model. Later on, on a more complex three-lane section, we trained the integrated model combines both car-following and lane-changing behavior, the average speed further grows 2.4%. It indicates that our framework is effective for Automated vehicle's decision-making learning.
Collective Attention towards Scientists and Research Topics
Venue: arXiv cs.CY 2018
Year: 2018

Emergent patterns of collective attention towards scientists and their research may function as a proxy for scientific impact which traditionally is assessed via committees that award prizes to scientists. Therefore it is crucial to understand the relationships between scientific impact and online demand and supply for information about scientists and their work. In this paper, we compare the temporal pattern of information supply (article creations) and information demand (article views) on Wikipedia for two groups of scientists: scientists who received one of the most prestigious awards in their field and influential scientists from the same field who did not receive an award. Our research highlights that awards function as external shocks which increase supply and demand for information about scientists, but hardly affect information supply and demand for their research topics. Further, we find interesting differences in the temporal ordering of information supply between the two groups: (i) award-winners have a higher probability that interest in them precedes interest in their work; (ii) for award winners interest in articles about them and their work is temporally more clustered than for non-awarded scientists.